Yinglong Li | Deep Learning | Research Excellence Award

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Dr. Yinglong Li | Deep Learning | Research Excellence Award

Associate Professor | Zhejiang University of Technology |China

Yinglong Li demonstrates strong academic and research excellence, supported by a solid portfolio of publications, patents, and externally funded projects. His 22 SCI/Scopus-indexed journal papers, more than 140 Web of Science citations, and a steadily increasing h-index reflect both productivity and growing scholarly influence. His contributions are further validated through documented outputs such as two published textbooks (ISBN: 9787302557425, 9787115415400), ten patents, and verified academic profiles including Google Scholar and institutional webpages. Strengths include his ability to integrate privacy protection, deep learning, and computer vision into practical AI solutions, as evidenced by consultancy projects with industry partners in marine systems and smart security. His research has consistently translated into deployable, high-impact technologies, demonstrating maturity in innovation and applied problem-solving. Areas for improvement include expanding international collaborations, enhancing cross-disciplinary engagement with emerging domains such as trustworthy AI governance, and increasing participation in editorial boards or leadership roles in prestigious conferences, which would further elevate his global visibility. Moving forward, his research has strong potential to contribute significantly to privacy-preserving intelligent systems, multimodal vision architectures, and secure data ecosystems for smart cities. With a well-documented research track record, growing citation metrics, and scalable research themes aligned with global technological needs, he is positioned for continued advancement and wider impact in the AI research community.

Citation Metrics (Google Scholar)

240180

120

60

0

Citations
234

h-index
8

i10-index
6

Citations
h-index
i10-index


View Google Scholar Profile

Featured Publications

Pei-Jun Lee | Edeg AI Design for Remote Sensing | Best Researcher Award

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Prof. Pei-Jun Lee | Edeg AI Design for Remote Sensing | Best Researcher Award

National Taiwan University of Sience and Techlogy | Taiwan

Pei-Jun Lee (IET Fellow, IEEE Senior Member) is a distinguished researcher whose work spans advanced video compression, FPGA-based edge AI computing, and remote sensing satellite imaging systems. Her research portfolio includes over 100 publications, multiple patents, and extensive industry–academia collaborations. She has led major technological innovations in AI-driven satellite imaging circuits, infrared sensor systems, and high-performance embedded architectures. Her work with the Taiwan Space Organization (TASA) on the Formosat-8 mission enabled the development of FPGA circuits for real-time moving object detection and onboard video compression, marking the first Taiwanese satellite with such capabilities. She also contributed to the CubeSat Key Technology R&D Project, resulting in the successful launch of Lilium-I, Taiwan’s first 3U CubeSat equipped with an AI remote sensing payload. Her research further includes optical and mechanical circuit design for infrared imaging modules, 2D/3D conversion systems, multi-view and stereoscopic display technologies, and low-complexity solutions for standards such as 3D-HEVC, H.264, and MPEG-4. Her applied innovations extend to patient-care imaging systems and bio-inspired robotic fish. Through sustained industry collaboration and competitive project achievements, her work demonstrates both strong theoretical innovation and significant impact on practical, space-grade imaging technologies.

Profile: Scopus | Orcid 

Featured Publications 

Kusumoseniarto, R. H., Lin, Z.-Y., Su, S.-F., & Lee, P.-J. (2025).
Real-time human action recognition with dynamical frame processing via modified ConvLSTM and BERT. IEEE Access.

Bui, T.-A., Lee, P.-J., Liobe, J., Barzdenas, V., & Udris, D. (2025).
Region of interest-focused dynamic enhancement (RoIDE) for satellite images. IEEE Transactions on Geoscience and Remote Sensing.

Hsu, C.-H., Lee, P.-J., & Bui, T.-A. (2025, March 29).
Lightweight feature-enhanced U-Net for landslide change detection in remote sensing imagery.
In Proceedings of the ICCT-Pacific 2025.

Chen, C.-F., Lee, P.-J., & Bui, T.-A. (2025, January 11).
Low parameters UNet for energy-efficient cloud detection.
In 2025 IEEE ICCE Conference Proceedings.

Su, R.-Y., & Lee, P.-J. (2025, January 11).
Tiny objects classification on remote sensing image by using multi-scale crop.
In 2025 IEEE ICCE Conference Proceedings.

Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

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Assoc. Prof. Dr. Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

Zhejiang A&F University | China 

Dr. Tongcun Liu is an Associate Professor at Zhejiang A & F University, specializing in computer science and technology with a strong focus on big data analytics and artificial intelligence. He earned his Ph.D. from the Beijing University of Posts and Telecommunications and later enhanced his academic experience as a Visiting Scholar at the Hong Kong University of Science and Technology. His research primarily revolves around advanced algorithms for graph computing, recommendation systems, and AI4Science, contributing significantly to the intersection of data intelligence and computational innovation. Dr. Liu leads multiple research projects funded by the National Natural Science Foundation of China and the Zhejiang Provincial Natural Science Foundation. His current and completed projects include the development of data-driven models for estimating mangrove soil dissolved organic carbon sequestration potential and the creation of cloud-edge collaborative recommendation systems based on session flow methods. With a robust publication record of more than 30 papers in esteemed international journals and conferences, his scholarly work has had a substantial impact on the field of artificial intelligence and data-driven computing. In addition to his academic achievements, Dr. Liu holds over 10 granted patents from more than 20 applications, reflecting his strong commitment to technological innovation and the advancement of AI-based computational methodologies.

Profile : Google Scholar

Featured Publications 

Feng, H., Qiu, J., Wen, L., Zhang, J., Yang, J., Lyu, Z., Liu, T., & Fang, K. (2025). U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision. Neural Networks, 185, 107207.

Fang, K., Deng, J., Dong, C., Naseem, U., Liu, T., Feng, H., & Wang, W. (2025). MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network. Proceedings of the ACM on Web Conference 2025, 5065–5074.

Liu, T., Yu, G., Kwok, H. Y., Xue, R., He, D., & Liang, W. (2025). Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration. Marine Environmental Research, 107170.

Shi, Q., Wang, Y., Liu, T., Zhang, L., & Liao, J. (2024). STRL: Writer-Independent Offline Signature Verification with Transformers and Self-Supervised Representation Learning. 2024 10th International Conference on Computer and Communications (ICCC).

Liu, T., Bao, X., Zhang, J., Fang, K., & Feng, H. (n.d.). Enhancing session-based recommendation with multi-interest hyperbolic representation networks. IEEE Transactions on Neural Networks and Learning Systems.

Dr. Agnieszka Niemczynowicz – Machine Learning – Best Researcher Award 

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Dr. Agnieszka Niemczynowicz - Machine Learning - Best Researcher Award 

Cracow University of technology - Poland

AUTHOR PROFILE 

ORCID 

EARLY ACADEMIC PURSUITS 🎓

Agnieszka Niemczynowicz began her academic journey in the field of solid-state physics, earning her Ph.D. from the Faculty of Physics and Applied Informatics at the University of Łódź, Poland, in 2014. Her early research laid a strong foundation in the fundamental aspects of physics, equipping her with a deep understanding of physical systems and analytical techniques.

PROFESSIONAL ENDEAVORS 🏢

Upon completing her doctorate, Agnieszka transitioned into academia, taking up the role of Associate Professor at the Cracow University of Technology. She has since been instrumental in bridging the gap between physics and computational sciences, expanding her research horizons to include computational and mathematical methods for analyzing complex data sets across various disciplines.

CONTRIBUTIONS AND RESEARCH FOCUS 🔍

Agnieszka’s research is at the forefront of computational analysis, focusing on multivariate statistics, chemometrics, and deep learning. She has developed advanced statistical and machine Machine Learning learning models that have found applications in diverse fields such as engineering, biology, medicine, and management. Her work is characterized by its interdisciplinary approach, integrating complex data analysis methods into practical applications.

ACCREDITATIONS AND RECOGNITION 🏅

A prolific researcher, Agnieszka has authored around 50 publications in international journals, contributing significantly to her field. Her excellence in research was recognized with the Machine Learning prestigious Doak Award in 2022, highlighting her impactful contributions to the scientific community and her role as a thought leader in computational analysis.

IMPACT AND INFLUENCE 🌍

Agnieszka’s work has had a significant impact on how complex analytical data is interpreted and utilized across various sectors. Her models have improved the accuracy of data-driven Machine Learning decisions in numerous applications, thereby enhancing the efficiency and effectiveness of processes in engineering, biology, medicine, and more.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Currently leading international research grants, Agnieszka investigates the mathematical foundations of hypercomplex neural networks and their applications. Her ongoing work promises to further unravel the complexities of data analysis, pushing the boundaries of what machine learning and computational methods can achieve. Her legacy lies in her pioneering efforts to integrate advanced mathematical models into practical solutions, ensuring that her influence will be felt across multiple disciplines for years to come.

NOTABLE PUBLICATIONS 

  • Title: A critical analysis of the theoretical framework of the Extreme Learning Machine
    Authors: Irina Perfilieva, Nicolás Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz
    Journal: Neurocomputing
  • Title: Use of physicochemical, FTIR and chemometric analysis for quality assessment of selected monofloral honeys
    Authors: Monika Kędzierska-Matysek, Anna Teter, Mariusz Florek, Arkadiusz Matwijczuk, Agnieszka Niemczynowicz, Alicja Matwijczuk, Grzegorz Czernel, Piotr Skałecki, Bożena Gładyszewska
    Journal: Journal of Apicultural Research
  • Title: Conclusions
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)
  • Title: Current research methods in mathematical and computer modelling of motivation management
    Authors: Agnieszka Niemczynowicz, Radosław Antoni Kycia
    Journal: (Book chapter, not a journal)
  • Title: Introduction
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)

Dr. Yosr Ghozzi – Deep learning – Excellence in Research

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Dr. Yosr Ghozzi - Deep learning - Excellence in Research

University of Sfax - Tunisia

AUTHOR PROFILE

SCOPUS

ORCID

EARLY ACADEMIC PURSUITS 🎓

Yosr Ghozzi embarked on her academic journey with a strong focus on technological innovation and medical applications. She earned her Ph.D. from the National Engineering School of Sfax (ENIS) in 2022, which marked the beginning of her career as a teacher-researcher. Her academic background is rooted in artificial intelligence, deep learning, and medical imaging, laying the foundation for her future contributions to the field.

PROFESSIONAL ENDEAVORS 🏥

Since 2022, Yosr has been a dedicated teacher and researcher at ISIMS/University of Sfax in Tunisia. Her work is characterized by a strong focus on artificial intelligence and its applications in medical diagnostics. Her role as a researcher has allowed her to contribute significantly to both academic and industrial projects, with a notable project being the KAFSS, which involves molecular kits and digital applications for facial prediction and dysmorphia.

CONTRIBUTIONS AND RESEARCH FOCUS 🔬

Yosr's research is at the intersection of artificial intelligence and medical imaging. She has made significant strides in the use of deep learning Deep learning combined with fuzzy logic to address the challenges of content-based image retrieval (CBIR) systems. Her work in this area is particularly relevant for computer-aided diagnosis, where CBIR systems can aid in detecting abnormalities and classifying medical images based on their content and medical context.

ACCREDITATIONS AND RECOGNITION 🏆

Yosr's contributions to research have not gone unnoticed. In 2022, she was awarded the prestigious title of Best Innovative Researcher at TICAD 8. Her research has also been published in two journals indexed by SCI and Scopus, and she has been involved in the editorial process of six Deep learning conferences, further establishing her as a recognized figure in her field.

IMPACT AND INFLUENCE 🌍

Yosr's research has a significant impact on the medical and technological fields, particularly in the development of advanced diagnostic tools. Her collaboration with international researchers from the University of Essex and the University of Johannesburg highlights her global influence. Her Deep learning work in AI and deep learning contributes to improving the accuracy and efficiency of medical diagnostics, potentially saving lives through early detection and intervention.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Yosr Ghozzi is set to leave a lasting legacy in the fields of artificial intelligence and medical imaging. Her innovative approaches to using deep learning for medical applications position her as a pioneer in her field. As she continues her research, Yosr's contributions will likely inspire future advancements in AI-driven healthcare solutions, ensuring her work's lasting impact on both academia and the medical community.

NOTABLE PUBLICATIONS

Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval 2022(13)

Competencies based curricula (Electro-Mechanical Department Case Study) 2019(1)

A beta-fuzzy-near-sets approach to research for visually similar content images 2017(3)

A novel method for resemblance images using near fuzzy set 2016(2)

Modelling and numerical simulation of thick sheet double slitting process using continuum 2014(7)